Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	13.0	42.463328
daal4py_KNeighborsClassifier	0.0	3.0	5.018768
KNeighborsClassifier_kd_tree	0.0	6.0	22.926067
daal4py_KNeighborsClassifier_kd_tree	0.0	1.0	43.130476
KMeans_tall	0.0	1.0	41.719820
daal4py_KMeans_tall	0.0	1.0	20.050662
KMeans_short	0.0	0.0	15.309606
daal4py_KMeans_short	0.0	0.0	7.423445
LogisticRegression	0.0	1.0	4.110969
daal4py_LogisticRegression	0.0	0.0	34.665548
Ridge	0.0	0.0	25.199624
daal4py_Ridge	0.0	0.0	6.653672
total	0.0	30.0	28.740353

KNeighborsClassifier vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	brute

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	n_jobs	n_neighbors	throughput	latency	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	0.143	0.003	1000000	1000000	100	-1	1	5.595	NaN	0.979	0.982	0.512	0.005	0.279	0.006	See	See
1	KNeighborsClassifier	predict	26.839	0.391	1000000	1000	100	-1	1	0.000	0.027	0.979	0.982	1.743	0.037	15.399	0.396	See	See
2	KNeighborsClassifier	predict	0.183	0.014	1000000	1	100	-1	1	0.004	0.000	0.979	0.982	0.098	0.001	1.864	0.145	See	See
3	KNeighborsClassifier	fit	0.142	0.001	1000000	1000000	100	-1	5	5.645	NaN	0.979	0.982	0.506	0.004	0.280	0.003	See	See
4	KNeighborsClassifier	predict	34.787	0.000	1000000	1000	100	-1	5	0.000	0.035	0.979	0.982	1.712	0.015	20.324	0.183	See	See
5	KNeighborsClassifier	predict	0.197	0.015	1000000	1	100	-1	5	0.004	0.000	0.979	0.982	0.092	0.001	2.147	0.162	See	See
6	KNeighborsClassifier	fit	0.145	0.002	1000000	1000000	100	-1	100	5.501	NaN	0.979	0.982	0.501	0.003	0.290	0.004	See	See
7	KNeighborsClassifier	predict	34.701	0.000	1000000	1000	100	-1	100	0.000	0.035	0.979	0.982	1.776	0.011	19.539	0.121	See	See
8	KNeighborsClassifier	predict	0.195	0.016	1000000	1	100	-1	100	0.004	0.000	0.979	0.982	0.094	0.002	2.067	0.174	See	See
9	KNeighborsClassifier	fit	0.143	0.001	1000000	1000000	100	1	1	5.594	NaN	0.979	0.982	0.518	0.005	0.276	0.004	See	See
10	KNeighborsClassifier	predict	13.993	0.028	1000000	1000	100	1	1	0.000	0.014	0.979	0.982	1.708	0.019	8.194	0.092	See	See
11	KNeighborsClassifier	predict	0.214	0.003	1000000	1	100	1	1	0.004	0.000	0.979	0.982	0.094	0.001	2.289	0.033	See	See
12	KNeighborsClassifier	fit	0.141	0.001	1000000	1000000	100	1	5	5.668	NaN	0.979	0.982	0.522	0.014	0.270	0.008	See	See
13	KNeighborsClassifier	predict	22.313	0.011	1000000	1000	100	1	5	0.000	0.022	0.979	0.982	1.728	0.038	12.909	0.285	See	See
14	KNeighborsClassifier	predict	0.216	0.003	1000000	1	100	1	5	0.004	0.000	0.979	0.982	0.096	0.001	2.249	0.040	See	See
15	KNeighborsClassifier	fit	0.154	0.002	1000000	1000000	100	1	100	5.193	NaN	0.979	0.982	0.520	0.003	0.296	0.005	See	See
16	KNeighborsClassifier	predict	22.229	0.081	1000000	1000	100	1	100	0.000	0.022	0.979	0.982	1.819	0.044	12.219	0.298	See	See
17	KNeighborsClassifier	predict	0.215	0.002	1000000	1	100	1	100	0.004	0.000	0.979	0.982	0.097	0.001	2.218	0.031	See	See
18	KNeighborsClassifier	fit	0.057	0.001	1000000	1000000	2	-1	1	0.282	NaN	0.979	0.982	0.101	0.002	0.559	0.016	See	See
19	KNeighborsClassifier	predict	23.569	0.096	1000000	1000	2	-1	1	0.000	0.024	0.979	0.982	0.255	0.000	92.423	0.395	See	See
20	KNeighborsClassifier	predict	0.020	0.001	1000000	1	2	-1	1	0.001	0.000	0.979	0.982	0.006	0.000	3.581	0.328	See	See
21	KNeighborsClassifier	fit	0.058	0.001	1000000	1000000	2	-1	5	0.278	NaN	0.979	0.982	0.101	0.002	0.571	0.012	See	See
22	KNeighborsClassifier	predict	32.105	0.000	1000000	1000	2	-1	5	0.000	0.032	0.979	0.982	0.258	0.001	124.673	0.391	See	See
23	KNeighborsClassifier	predict	0.029	0.001	1000000	1	2	-1	5	0.001	0.000	0.979	0.982	0.005	0.000	5.453	0.423	See	See
24	KNeighborsClassifier	fit	0.057	0.001	1000000	1000000	2	-1	100	0.280	NaN	0.979	0.982	0.099	0.001	0.580	0.011	See	See
25	KNeighborsClassifier	predict	32.045	0.000	1000000	1000	2	-1	100	0.000	0.032	0.979	0.982	0.304	0.001	105.497	0.213	See	See
26	KNeighborsClassifier	predict	0.031	0.002	1000000	1	2	-1	100	0.001	0.000	0.979	0.982	0.006	0.000	5.587	0.547	See	See
27	KNeighborsClassifier	fit	0.058	0.001	1000000	1000000	2	1	1	0.276	NaN	0.979	0.982	0.101	0.002	0.572	0.015	See	See
28	KNeighborsClassifier	predict	11.037	0.041	1000000	1000	2	1	1	0.000	0.011	0.979	0.982	0.256	0.001	43.107	0.229	See	See
29	KNeighborsClassifier	predict	0.015	0.001	1000000	1	2	1	1	0.001	0.000	0.979	0.982	0.006	0.000	2.634	0.254	See	See
30	KNeighborsClassifier	fit	0.057	0.001	1000000	1000000	2	1	5	0.280	NaN	0.979	0.982	0.101	0.001	0.567	0.006	See	See
31	KNeighborsClassifier	predict	19.847	0.015	1000000	1000	2	1	5	0.000	0.020	0.979	0.982	0.258	0.001	76.835	0.294	See	See
32	KNeighborsClassifier	predict	0.024	0.001	1000000	1	2	1	5	0.001	0.000	0.979	0.982	0.005	0.000	4.448	0.371	See	See
33	KNeighborsClassifier	fit	0.057	0.001	1000000	1000000	2	1	100	0.278	NaN	0.979	0.982	0.101	0.003	0.572	0.019	See	See
34	KNeighborsClassifier	predict	19.929	0.033	1000000	1000	2	1	100	0.000	0.020	0.979	0.982	0.305	0.003	65.378	0.644	See	See
35	KNeighborsClassifier	predict	0.025	0.001	1000000	1	2	1	100	0.001	0.000	0.979	0.982	0.005	0.001	4.699	0.492	See	See

KNeighborsClassifier_kd_tree vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	kd_tree

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	n_jobs	n_neighbors	throughput	latency	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	3.173	0.070	1000000	1000000	10	-1	1	0.025	NaN	0.981	0.989	0.706	0.017	4.495	0.145	See	See
1	KNeighborsClassifier_kd_tree	predict	0.460	0.005	1000000	1000	10	-1	1	0.000	0.000	0.981	0.989	0.108	0.001	4.283	0.054	See	See
2	KNeighborsClassifier_kd_tree	predict	0.005	0.006	1000000	1	10	-1	1	0.017	0.000	0.981	0.989	0.000	0.000	18.309	25.694	See	See
3	KNeighborsClassifier_kd_tree	fit	3.167	0.057	1000000	1000000	10	-1	5	0.025	NaN	0.981	0.989	0.723	0.004	4.382	0.082	See	See
4	KNeighborsClassifier_kd_tree	predict	0.860	0.027	1000000	1000	10	-1	5	0.000	0.001	0.981	0.989	0.197	0.002	4.359	0.145	See	See
5	KNeighborsClassifier_kd_tree	predict	0.004	0.001	1000000	1	10	-1	5	0.020	0.000	0.981	0.989	0.000	0.000	13.192	6.322	See	See
6	KNeighborsClassifier_kd_tree	fit	3.165	0.086	1000000	1000000	10	-1	100	0.025	NaN	0.981	0.989	0.702	0.014	4.506	0.154	See	See
7	KNeighborsClassifier_kd_tree	predict	2.863	0.028	1000000	1000	10	-1	100	0.000	0.003	0.981	0.989	0.599	0.004	4.781	0.055	See	See
8	KNeighborsClassifier_kd_tree	predict	0.008	0.001	1000000	1	10	-1	100	0.011	0.000	0.981	0.989	0.001	0.000	11.386	4.835	See	See
9	KNeighborsClassifier_kd_tree	fit	3.199	0.078	1000000	1000000	10	1	1	0.025	NaN	0.981	0.989	0.737	0.008	4.343	0.116	See	See
10	KNeighborsClassifier_kd_tree	predict	0.778	0.009	1000000	1000	10	1	1	0.000	0.001	0.981	0.989	0.113	0.003	6.892	0.200	See	See
11	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	1	1	0.063	0.000	0.981	0.989	0.000	0.000	4.880	2.556	See	See
12	KNeighborsClassifier_kd_tree	fit	3.223	0.073	1000000	1000000	10	1	5	0.025	NaN	0.981	0.989	0.730	0.030	4.414	0.207	See	See
13	KNeighborsClassifier_kd_tree	predict	1.510	0.008	1000000	1000	10	1	5	0.000	0.002	0.981	0.989	0.209	0.003	7.226	0.121	See	See
14	KNeighborsClassifier_kd_tree	predict	0.002	0.001	1000000	1	10	1	5	0.038	0.000	0.981	0.989	0.000	0.000	6.835	3.322	See	See
15	KNeighborsClassifier_kd_tree	fit	3.080	0.050	1000000	1000000	10	1	100	0.026	NaN	0.981	0.989	0.758	0.024	4.063	0.147	See	See
16	KNeighborsClassifier_kd_tree	predict	5.140	0.043	1000000	1000	10	1	100	0.000	0.005	0.981	0.989	0.634	0.009	8.105	0.133	See	See
17	KNeighborsClassifier_kd_tree	predict	0.006	0.001	1000000	1	10	1	100	0.013	0.000	0.981	0.989	0.001	0.000	8.941	3.887	See	See
18	KNeighborsClassifier_kd_tree	fit	0.785	0.016	1000000	1000000	2	-1	1	0.020	NaN	0.981	0.989	0.476	0.007	1.649	0.040	See	See
19	KNeighborsClassifier_kd_tree	predict	0.026	0.000	1000000	1000	2	-1	1	0.001	0.000	0.981	0.989	0.001	0.000	33.859	12.469	See	See
20	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	-1	1	0.007	0.000	0.981	0.989	0.000	0.000	19.166	16.936	See	See
21	KNeighborsClassifier_kd_tree	fit	0.778	0.016	1000000	1000000	2	-1	5	0.021	NaN	0.981	0.989	0.470	0.007	1.657	0.042	See	See
22	KNeighborsClassifier_kd_tree	predict	0.027	0.000	1000000	1000	2	-1	5	0.001	0.000	0.981	0.989	0.001	0.000	23.997	7.289	See	See
23	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	-1	5	0.007	0.000	0.981	0.989	0.000	0.000	20.032	17.363	See	See
24	KNeighborsClassifier_kd_tree	fit	0.782	0.011	1000000	1000000	2	-1	100	0.020	NaN	0.981	0.989	0.468	0.004	1.670	0.028	See	See
25	KNeighborsClassifier_kd_tree	predict	0.046	0.000	1000000	1000	2	-1	100	0.000	0.000	0.981	0.989	0.006	0.001	7.030	0.725	See	See
26	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	-1	100	0.007	0.000	0.981	0.989	0.000	0.000	17.504	13.365	See	See
27	KNeighborsClassifier_kd_tree	fit	0.763	0.011	1000000	1000000	2	1	1	0.021	NaN	0.981	0.989	0.468	0.005	1.630	0.030	See	See
28	KNeighborsClassifier_kd_tree	predict	0.024	0.000	1000000	1000	2	1	1	0.001	0.000	0.981	0.989	0.001	0.000	32.561	10.409	See	See
29	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	1	1	0.027	0.000	0.981	0.989	0.000	0.000	5.361	4.926	See	See
30	KNeighborsClassifier_kd_tree	fit	0.746	0.003	1000000	1000000	2	1	5	0.021	NaN	0.981	0.989	0.464	0.005	1.607	0.017	See	See
31	KNeighborsClassifier_kd_tree	predict	0.025	0.000	1000000	1000	2	1	5	0.001	0.000	0.981	0.989	0.001	0.000	23.494	7.475	See	See
32	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	1	5	0.027	0.000	0.981	0.989	0.000	0.000	5.296	4.908	See	See
33	KNeighborsClassifier_kd_tree	fit	0.759	0.009	1000000	1000000	2	1	100	0.021	NaN	0.981	0.989	0.469	0.012	1.619	0.045	See	See
34	KNeighborsClassifier_kd_tree	predict	0.055	0.000	1000000	1000	2	1	100	0.000	0.000	0.981	0.989	0.007	0.001	8.186	0.812	See	See
35	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	1	100	0.026	0.000	0.981	0.989	0.000	0.000	5.132	4.293	See	See

KMeans_tall vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	init	n_iter_sklearn	throughput	latency	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	0.580	0.005	1000000	1000000	2	k-means++	30	0.827	NaN	0.002	30	0.002	0.404	0.027	1.435	0.096	See	See
1	KMeans_tall	predict	0.001	0.000	1000000	1000	2	k-means++	30	0.012	0.0	0.002	30	0.002	0.000	0.000	8.578	6.343	See	See
2	KMeans_tall	predict	0.001	0.000	1000000	1	2	k-means++	30	0.013	0.0	0.002	30	0.002	0.000	0.000	10.842	8.611	See	See
3	KMeans_tall	fit	0.514	0.006	1000000	1000000	2	random	30	0.933	NaN	0.002	30	0.002	0.366	0.023	1.407	0.091	See	See
4	KMeans_tall	predict	0.001	0.000	1000000	1000	2	random	30	0.013	0.0	0.002	30	0.002	0.000	0.000	9.188	6.836	See	See
5	KMeans_tall	predict	0.001	0.000	1000000	1	2	random	30	0.013	0.0	0.002	30	0.002	0.000	0.000	10.266	8.052	See	See
6	KMeans_tall	fit	6.270	0.114	1000000	1000000	100	k-means++	30	3.828	NaN	0.002	30	0.002	3.200	0.017	1.959	0.037	See	See
7	KMeans_tall	predict	0.002	0.000	1000000	1000	100	k-means++	30	0.514	0.0	0.002	30	0.002	0.000	0.000	5.729	3.040	See	See
8	KMeans_tall	predict	0.001	0.000	1000000	1	100	k-means++	30	0.645	0.0	0.002	30	0.002	0.000	0.000	10.149	7.759	See	See
9	KMeans_tall	fit	5.858	0.039	1000000	1000000	100	random	30	4.097	NaN	0.002	30	0.002	3.120	0.074	1.877	0.046	See	See
10	KMeans_tall	predict	0.002	0.000	1000000	1000	100	random	30	0.512	0.0	0.002	30	0.002	0.000	0.000	5.683	3.166	See	See
11	KMeans_tall	predict	0.001	0.000	1000000	1	100	random	30	0.647	0.0	0.002	30	0.002	0.000	0.000	10.056	7.540	See	See

KMeans_short vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	20
n_init	1
tol	0.0

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	init	n_iter_sklearn	throughput	latency	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	0.218	0.002	10000	10000	2	k-means++	20	0.015	NaN	0.303	20	0.318	0.084	0.001	2.584	0.052	See	See
1	KMeans_short	predict	0.002	0.000	10000	1000	2	k-means++	20	0.010	0.0	0.303	20	0.318	0.001	0.000	2.690	0.634	See	See
2	KMeans_short	predict	0.001	0.000	10000	1	2	k-means++	20	0.013	0.0	0.303	20	0.318	0.000	0.000	9.236	7.285	See	See
3	KMeans_short	fit	0.074	0.002	10000	10000	2	random	20	0.043	NaN	0.303	20	0.318	0.029	0.000	2.557	0.090	See	See
4	KMeans_short	predict	0.002	0.000	10000	1000	2	random	20	0.010	0.0	0.303	20	0.318	0.001	0.000	2.632	0.589	See	See
5	KMeans_short	predict	0.001	0.000	10000	1	2	random	20	0.013	0.0	0.303	20	0.318	0.000	0.000	7.402	4.288	See	See
6	KMeans_short	fit	0.597	0.016	10000	10000	100	k-means++	20	0.268	NaN	0.303	20	0.318	0.318	0.003	1.878	0.053	See	See
7	KMeans_short	predict	0.003	0.000	10000	1000	100	k-means++	20	0.319	0.0	0.303	20	0.318	0.001	0.000	1.979	0.605	See	See
8	KMeans_short	predict	0.001	0.000	10000	1	100	k-means++	20	0.594	0.0	0.303	20	0.318	0.000	0.000	8.169	4.983	See	See
9	KMeans_short	fit	0.203	0.002	10000	10000	100	random	20	0.789	NaN	0.303	20	0.318	0.131	0.002	1.550	0.029	See	See
10	KMeans_short	predict	0.002	0.000	10000	1000	100	random	20	0.337	0.0	0.303	20	0.318	0.001	0.000	2.146	0.389	See	See
11	KMeans_short	predict	0.001	0.000	10000	1	100	random	20	0.601	0.0	0.303	20	0.318	0.000	0.000	7.987	4.647	See	See

LogisticRegression vs daal4py

All estimators share the following hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	class_weight	l1_ratio	n_jobs	random_state	n_iter	throughput	latency	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	11.452	0.041	1000000	1000000	100	NaN	NaN	NaN	NaN	[20]	[-0.10302895]	NaN	0.24	2.123	0.024	5.395	0.063	See	See
1	LogisticRegression	predict	0.000	0.000	1000000	1000	100	NaN	NaN	NaN	NaN	[20]	2.4825862096241766	0.0	0.24	0.000	0.000	0.902	0.526	See	See
2	LogisticRegression	predict	0.000	0.000	1000000	1	100	NaN	NaN	NaN	NaN	[20]	10.891599986003278	0.0	0.24	0.000	0.000	0.418	0.402	See	See
3	LogisticRegression	fit	0.824	0.007	1000	1000	10000	NaN	NaN	NaN	NaN	[27]	[-2.59056809]	NaN	0.24	0.849	0.032	0.971	0.038	See	See
4	LogisticRegression	predict	0.002	0.000	1000	100	10000	NaN	NaN	NaN	NaN	[27]	4.531691848893503	0.0	0.24	0.003	0.000	0.583	0.120	See	See
5	LogisticRegression	predict	0.000	0.000	1000	1	10000	NaN	NaN	NaN	NaN	[27]	77.90331028750583	0.0	0.24	0.001	0.000	0.140	0.100	See	See

Ridge vs daal4py

All estimators share the following hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	deprecated
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	max_iter	random_state	throughput	latency	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	0.183	0.001	1000	1000	10000	NaN	NaN	0.437	NaN	1.0	0.190	0.002	0.962	0.014	See	See
1	Ridge	predict	0.013	0.000	1000	1000	10000	NaN	NaN	6.158	0.0	1.0	0.021	0.001	0.616	0.032	See	See
2	Ridge	predict	0.000	0.000	1000	1	10000	NaN	NaN	1171.114	0.0	1.0	0.000	0.000	0.633	0.697	See	See
3	Ridge	fit	1.214	0.073	1000000	1000000	100	NaN	NaN	0.659	NaN	1.0	0.240	0.005	5.051	0.323	See	See
4	Ridge	predict	0.000	0.000	1000000	1000	100	NaN	NaN	5.085	0.0	1.0	0.000	0.000	0.669	0.495	See	See
5	Ridge	predict	0.000	0.000	1000000	1	100	NaN	NaN	12.426	0.0	1.0	0.000	0.000	0.664	0.722	See	See

Environment information

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": null,
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}